The study was divided into two phases. During the first phase we used existing data to model the decision to increase hypoglycemic medications. At each medical visit, we determined whether an increase in medication therapy occurred. We then used recursive partitioning to develop a model that identified patient characteristics at the visit, such as recent laboratory results and diagnoses, associated with the decision to increase therapy. This model assigns a predicted probability of an increase in therapy to each visit. We used these predictions to define an intensity of hypoglycemic medication therapy for each physician that compared the actual to predicted number of increases over all patient-visits. The second phase was a randomized trial in which clinicians at experimental sites receive feedback on performance and access to expert opinion while usual care is provided at control sites. Feedback on performance was provided twice over 6 months. The change in intensity of treatment scores and glycosylated hemoglobin levels pre- and post-intervention at these sites were compared to performance of primary care physicians at control sites not receiving the intervention.